Knowledge extraction from neural networks for signal interpretation
Identifieur interne : 00BD21 ( Main/Exploration ); précédent : 00BD20; suivant : 00BD22Knowledge extraction from neural networks for signal interpretation
Auteurs : F. Alexandre [France] ; J.-F. Remm [France]Source :
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
Artificial neural networks have proved their ability to perform classification tasks. This ability is not satisfactory when expertise of the application domain is not available or when experts want to know more about hints that led to the decision. This leads presently to a great amount of work for knowledge or rule extraction from neural networks. In this paper, we propose a technique able to extract rules and to explain the functioning of the hidden layers of a multilayer perceptron. The first step consists in pruning the network with the classical OBD algorithm. Then, tightening of the sigmoidal transfer function can simply result in such knowledge extraction. This principle has been first tested on an application of signal interpretation in the radar domain.
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream PascalFrancis, to step Corpus: 000C05
- to stream PascalFrancis, to step Curation: 000C69
- to stream PascalFrancis, to step Checkpoint: 000C24
- to stream Main, to step Merge: 00C521
- to stream Main, to step Curation: 00BD21
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Knowledge extraction from neural networks for signal interpretation</title><author><name sortKey="Alexandre, F" sort="Alexandre, F" uniqKey="Alexandre F" first="F." last="Alexandre">F. Alexandre</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>CRIN-INRIA, BP 239 </s1><s2>54506 Vandoeuvre </s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Vandoeuvre </settlement></placeName></affiliation></author><author><name sortKey="Remm, J F" sort="Remm, J F" uniqKey="Remm J" first="J.-F." last="Remm">J.-F. Remm</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>CRIN-INRIA, BP 239 </s1><s2>54506 Vandoeuvre </s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Vandoeuvre </settlement></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">98-0230852</idno><date when="1997">1997</date><idno type="stanalyst">PASCAL 98-0230852 INIST</idno><idno type="RBID">Pascal:98-0230852</idno><idno type="wicri:Area/PascalFrancis/Corpus">000C05</idno><idno type="wicri:Area/PascalFrancis/Curation">000C69</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">000C24</idno><idno type="wicri:explorRef" wicri:stream="PascalFrancis" wicri:step="Checkpoint">000C24</idno><idno type="wicri:Area/Main/Merge">00C521</idno><idno type="wicri:Area/Main/Curation">00BD21</idno><idno type="wicri:Area/Main/Exploration">00BD21</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Knowledge extraction from neural networks for signal interpretation</title><author><name sortKey="Alexandre, F" sort="Alexandre, F" uniqKey="Alexandre F" first="F." last="Alexandre">F. Alexandre</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>CRIN-INRIA, BP 239 </s1><s2>54506 Vandoeuvre </s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Vandoeuvre </settlement></placeName></affiliation></author><author><name sortKey="Remm, J F" sort="Remm, J F" uniqKey="Remm J" first="J.-F." last="Remm">J.-F. Remm</name><affiliation wicri:level="3"><inist:fA14 i1="01"><s1>CRIN-INRIA, BP 239 </s1><s2>54506 Vandoeuvre </s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region><settlement type="city">Vandoeuvre </settlement></placeName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Information interpretation</term><term>Knowledge extraction</term><term>Multilayer perceptron</term><term>Neural network</term><term>Radar</term><term>Signal theory</term><term>Target</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Radar</term><term>Réseau neuronal</term><term>Théorie signal</term><term>Interprétation information</term><term>Cible</term><term>Perceptron multicouche</term><term>Extraction connaissance</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Artificial neural networks have proved their ability to perform classification tasks. This ability is not satisfactory when expertise of the application domain is not available or when experts want to know more about hints that led to the decision. This leads presently to a great amount of work for knowledge or rule extraction from neural networks. In this paper, we propose a technique able to extract rules and to explain the functioning of the hidden layers of a multilayer perceptron. The first step consists in pruning the network with the classical OBD algorithm. Then, tightening of the sigmoidal transfer function can simply result in such knowledge extraction. This principle has been first tested on an application of signal interpretation in the radar domain.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Vandoeuvre </li></settlement></list><tree><country name="France"><region name="Grand Est"><name sortKey="Alexandre, F" sort="Alexandre, F" uniqKey="Alexandre F" first="F." last="Alexandre">F. Alexandre</name></region><name sortKey="Remm, J F" sort="Remm, J F" uniqKey="Remm J" first="J.-F." last="Remm">J.-F. Remm</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 00BD21 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 00BD21 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Lorraine
|area= InforLorV4
|flux= Main
|étape= Exploration
|type= RBID
|clé= Pascal:98-0230852
|texte= Knowledge extraction from neural networks for signal interpretation
}}
| This area was generated with Dilib version V0.6.33. |  |